extended-abstract

Scalable methods to collect and visualize sidewalk accessibility data for people with mobility impairments

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Kotaro HaraAuthors Info & Claims

UIST '14 Adjunct: Adjunct Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

Pages 1 - 4

https://doi.org/10.1145/2658779.2661163

Published: 05 October 2014 Publication History

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Abstract

Poorly maintained sidewalks pose considerable accessibility challenges for mobility impaired persons; however, there are currently few, if any, mechanisms to determine accessible areas of a city a priori. In this paper, I introduce four threads of research that I will conduct for my Ph.D. thesis aimed at creating new methods and tools to provide unprecedented levels of information on the accessibility of streets and sidewalk. Namely, I will (i) conduct a formative study to better understand accessibility problems, (ii) develop and evaluate scalable map-based data collection methods, (iii) integrate computer vision algorithms to increase the scalability of the methods, and (iv) develop accessible-aware map-based tools that demonstrate the utility of our data (Figure 1 and 6).

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Cited By

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Kabir KAlsaleem AWiese J(2021)The Impact of Spinal Cord Injury on Participation in Human-Centered ResearchProceedings of the 2021 ACM Designing Interactive Systems Conference10.1145/3461778.3462122(1902-1914)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3461778.3462122
Watanabe TTakahashi HSato GIwasawa YMatsuo YYairi I(2021)Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural NetworksDeep Learning for Human Activity Recognition10.1007/978-981-16-0575-8_2(16-29)Online publication date: 18-Feb-2021
https://doi.org/10.1007/978-981-16-0575-8_2
Sato GWatanabe TTakahashi HYano YIwasawa YYairi I(2021)Visualizing Road Condition Information by Applying the AutoEncoder to Wheelchair Sensing Data for Road Barrier AssessmentAdvances in Artificial Intelligence10.1007/978-3-030-73113-7_2(13-24)Online publication date: 23-Jul-2021
https://doi.org/10.1007/978-3-030-73113-7_2
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Index Terms

Scalable methods to collect and visualize sidewalk accessibility data for people with mobility impairments
1. Human-centered computing

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Published In

UIST '14 Adjunct: Adjunct Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

October 2014

150 pages

ISBN:9781450330688

DOI:10.1145/2658779

General Chair:
Hrvoje Benko
Microsoft Research, USA
,
Program Chairs:
Mira Dontcheva
Adobe, USA
,
Daniel Wigdor
University of Toronto, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 October 2014

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UIST '14

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UIST '14: The 27th Annual ACM Symposium on User Interface Software and Technology

October 5 - 8, 2014

Hawaii, Honolulu, USA

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UIST '14 Adjunct Paper Acceptance Rate 74 of 333 submissions, 22%;

Overall Acceptance Rate 355 of 1,733 submissions, 20%

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UIST '25

Sponsor:
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The 38th Annual ACM Symposium on User Interface Software and Technology

September 28 - October 1, 2025

Busan , Republic of Korea

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Cited By

View all

Kabir KAlsaleem AWiese J(2021)The Impact of Spinal Cord Injury on Participation in Human-Centered ResearchProceedings of the 2021 ACM Designing Interactive Systems Conference10.1145/3461778.3462122(1902-1914)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3461778.3462122
Watanabe TTakahashi HSato GIwasawa YMatsuo YYairi I(2021)Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural NetworksDeep Learning for Human Activity Recognition10.1007/978-981-16-0575-8_2(16-29)Online publication date: 18-Feb-2021
https://doi.org/10.1007/978-981-16-0575-8_2
Sato GWatanabe TTakahashi HYano YIwasawa YYairi I(2021)Visualizing Road Condition Information by Applying the AutoEncoder to Wheelchair Sensing Data for Road Barrier AssessmentAdvances in Artificial Intelligence10.1007/978-3-030-73113-7_2(13-24)Online publication date: 23-Jul-2021
https://doi.org/10.1007/978-3-030-73113-7_2
Watanabe TTakahashi HIwasawa YMatsuo YEguchi Yairi I(2019)Weakly Supervised Learning for Evaluating Road Surface Condition from Wheelchair Driving DataInformation10.3390/info1101000211:1(2)Online publication date: 19-Dec-2019
https://doi.org/10.3390/info11010002
Yairi ITakahashi HWatanabe TNagamine KFukushima YMatsuo YIwasawa Y(2019)Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning TechnologiesInformation10.3390/info1003011410:3(114)Online publication date: 15-Mar-2019
https://doi.org/10.3390/info10030114
IWASAWA YEGUCHI YAIRI IMATSUO Y(2016)Combining Human Action Sensing of Wheelchair Users and Machine Learning for Autonomous Accessibility Data CollectionIEICE Transactions on Information and Systems10.1587/transinf.2015EDP7278E99.D:4(1153-1161)Online publication date: 2016
https://doi.org/10.1587/transinf.2015EDP7278
Chacra DZelek J(2016)Road Segmentation in Street View Images Using Texture Information2016 13th Conference on Computer and Robot Vision (CRV)10.1109/CRV.2016.47(424-431)Online publication date: Jun-2016
https://doi.org/10.1109/CRV.2016.47
Iwasawa YNagamine KYairi IMatsuo Y(2015)Toward an Automatic Road Accessibility Information Collecting and Sharing Based on Human Behavior Sensing Technologies of Wheelchair UsersProcedia Computer Science10.1016/j.procs.2015.08.31463(74-81)Online publication date: 2015
https://doi.org/10.1016/j.procs.2015.08.314

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